Data%20Collection%20and%20Sampling

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Data Collection and Sampling
2
Primary Data

• There are various methods for collecting primary
  (original) data
• Eg questionnaire, survey, interview, observation
• Control over investigation much greater
• Can more easily avoid data-driven research
• Cost can be prohibitive
• Pilot studies can be very helpful

3
Choice of method

• Shipman choice often between sampling and case
  study
• Intensive versus extensive research design
• Qualitative versus quantitative data
• Interpretivists favour the former positivists
  favour the latter
• All primary research involves selection
• Most methods require sampling

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Sampling general principles

• No a priori superiority of any method
• Trade-offs standardisation versus control,
  generalisability versus flexibility
• Shipman sampling method used dependent on nature
  of study undertaken
• Basis for sample must be transparent
• Cost of surveying entire population is
  prohibitive (e.g. census)
• Constraint of feasibility

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Sampling definitions

• Population must be defined
• Finite population e.g. voters
• Sampling unit single potential member of sample
• Sampling frame list of sampling units (NB 1936
  US Presidential election)
• Sample drawn from sampling frame

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Probability Sampling

• Probability of each sampling unit being chosen is
  known (often equal probability)
• Simple random sampling classic method, regarded
  as most reliable, least biased
• List numbered sampling frame members and select
  via random number generator
• Other probabilistic methods are available

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Systematic sampling

• List members of sampling frame
• Choose first sample member randomly
• Then choose every Kth unit, where KN/n
• More convenient than SRS for large popn
• Can be a systematic pattern in sample list,
  leading to bias e.g. corner shops

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Stratified sampling

• Divide population into groups of alike members
• Strata sizes usually proportionate to popn
• Draw randomly from groups
• Cost effective
• Ensure representativeness
• Can lead to excessive number of sub-groups

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Cluster Sampling

• Select large groups
• Select sampling units from clusters randomly
• Example take a city, divide into areas, number
  areas, select areas randomly, number units within
  areas, select units randomly
• Very cost-effective
• Very good if sampling frame poorly defined

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Non-probability Sampling

• Convenience sampling select whoever is available
• Quota sampling collect data according to
  proportions of the population
• Selection of subjects absolutely crucial
• Requires great skill of interviewers
• Snowball sampling select next subject from
  previous subject

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Non-Probability Sampling

• Theoretical sampling select those most likely to
  be affected by an issue
• Can ignore things which do not fit
• Can interpret observations according to the
  theory
• Non-prob sampling cannot claim representativeness
  as easily but gives much more discretion and
  control

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Response Rates

• Another possible trade-off is on response rates
• R 1 - (n-r)/n
• Even if initial sample size is appropriate (n
  n/(1(n/N)) where n s2/SE2 see F-N and N
  194-9) response rates can be low
• Postal questionnaires typically 20-40
• Non-response bias

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Response Rates

• Non-respondents could affect findings
• If reason for non-response is related to issue
  e.g. reluctance to interview drunks hampers study
  on alcoholism
• Response rate can be improved by cover letter,
  callbacks, skill of researcher, length of
  questionnaire, types of question

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Conclusions

• All types of primary data require selection
• If sampling used various methods possible
• Sampling method relates to research tool
• Different data collection techniques
  questionnaires, interviews, etc. - all to be
  studied in Research Methods 2 - all have
  advantages and disadvantages

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Secondary Data

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Introduction

• Primary quantitative data has several advantages,
  particularly control qualitative data too
• Do not equate primary and qualitative
• Today advantages of secondary data
• Searching on electronic data sources including
  the Internet

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Secondary data

• Primary/secondary is not qualitative/quantitativ
  e
• Qualitative can include secondary data sources
  such as personal documents, auto/biographies,
  etc.
• Secondary collected by someone else, e.g.
  another academic researcher, business, government
  agency, etc.

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Secondary data

• Used extensively in social science
• Durkheim suicide
• Marx wages, incomes, prices
• Weber church records
• Economists mainly use secondary data

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Advantages of Secondary Data

• Might be the only data available
• Enables longitudinal /time series work
• Cheaper (cost and time) and more convenient than
  primary data
• Aids generalisation
• Arises from natural settings (nonreactive/unobtrus
  ive data)
• Allows replication and checking - validity

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Disadvantages of Secondary Data

• May be not exactly the data required
• Differences in underlying sampling, design,
  questions asked, method of ascertaining
  information, etc.
• Differences lead to bias
• Method of data generation crucial to econometric
  studies

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Electronic Data Sources

• Through the library system
• Through the internet
• Known versus unknown sources
• Known sources via library catalogue
• Problem of reliability/credibility is common to
  all electronic sources (more than non-electronic
  sources)

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Electronic Data - Literature

• You can search by author or subject across
  journals, via several static websites/portals
• www.econlit.org/
• www.sosig.ac.uk
• www.mimas.ac.uk
• www.economics.ltsn.ac.uk
• www.esds.ac.uk

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Electronic Data Databases

• There are many databases available online
• Most have standardised, national data free to
  download in various formats
• Common file format is .csv but .html and even
  .xls files also common

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• OECD
• ONS
• UN
• Penn World Tables
• BEA (US)
• Ameristat
• Eurostat
• World Bank
• CIA
• US Statistical Abstract
• See Dissertation homepage/hb

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Conclusions

• Secondary data has many advantages and
  disadvantages relative to primary
• There is a wide range of secondary data available
• Much data is available on the internet
• Internet sources must be scrutinised more closely
  than other sources

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Qualitative Data
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Introduction

• Principals of research design and sampling
  basically hold for quantitative and qualitative
  data
• However, they apply most easily to quantitative
  analysis
• Qualitative analysis has different foci
• Qualitative analysis relatively (to quant other
  soc sci) unused in economics

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Qualitative techniques types

• Case study
• Fieldwork (ethnography)
• Observation
• Unstructured interviews
• Analytic induction/grounded theory
• Discourse analysis
• Theoretical sampling

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Qualitative techniques principals

• Qual often not quantitative
• Can use quant for pattern detection, qual for
  causal analysis
• Or use qual and quant as equals in inference
  (triangulation)
• Quantification often inappropriate

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Qualitative techniques principals

• Interpretivism, verstehen
• Used to be associated only with using
  autobiography, letters, personal documents,
  diaries
• Ethnography fairly recent
• Focus on cases rather than generality

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Qualitative techniques principals

• Analysis not really a separate stage of research
• Design, data collection and analysis all
  simultaneous and continuous
• Open-ended approach Theory and conclusions
  formed iteratively
• Imagination is crucial
• Recognise importance of exceptions
• Context is crucial

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Fieldwork

• Study of people acting in their daily lives
• Access a group but remain somewhat detached
• Approach with key questions
• Teams get range of perspectives
• Danger of self-perception and bias

33
Participant Observation

• Adopt perspectives of subject group in order to
  understand them
• Learning language, customs, behaviours, work,
  leisure, etc.
• Hanging around and learning the ropes
• Being an outsider can changes subjects behaviour
• Complete participation - researcher wholly
  concealed ? contamination and artificiality

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Participant Observation

• Researchers can go native (internalise group
  lifestyle)
• Covert researchers can be in danger or create
  detrimental behaviour
• Researchers can be piggy in the middle
• Covert recording observations can be difficult
  (e.g. need hidden cameras)
• Serious ethical issues with covert observation

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Employ analytic induction

• Go in with prejudices and theories
• Revise theory in light of evidence
• Generate new theories until evidence seems to fit
• Flexibility accorded but also required by the
  researcher
• Need to be open to disconfirming cases

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Grounded theory

• Data collected
• Develop categories (with inevitable theoretical
  priors and language)
• Categories checked by data
• Once categories seem secure and grounded in the
  evidence, formulate interconnection between
  categories

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Evaluation

• Broad range of qualitative techniques
• Control over the investigation less data driven
  flexibility much greater than quantitative
  studies
• Logistically difficult Huge amounts of data
  produced and problems with manipulation (although
  Nvivo will help with this)
• Must be careful to collect evidence widely to
  avoid bias
• Can be ethical issues re data collection and
  reporting